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Controller Design for Time-varying Sampling, Co-Regulated Systems

机译:时变采样,共调节系统的控制器设计

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“Co-regulation” is a time-varying periodic sampling strategy wherein the sampling rate is dynamically adjusted in response to the performance of the controlled system. The controller for co-regulated system needs to adjust control outputs corresponding to the current (changing) sampling rate. This makes performance guarantees such as stability difficult to obtain. In this paper we develop two stability guaranteed control algorithms for co-regulated systems. First is a correct-by-construction stabilizing controller where the control gain matrices are pre-computed offline for a set of sampling rates. This method allows for arbitrary switching of the sampling rates but as a result can be overly conservative. Then a hybrid Model Predictive Control (MPC) algorithm is tailored for co-regulated systems where both the system state trajectory and the sampling rate (scheduling parameter) trajectory are predicted within the receding horizon. The performances of the proposed controllers are demonstrated and discussed for a co-regulated multicopter Unmanned Aircraft System (UAS). The results show co-regulation can efficiently reallocate computational resources based on control performance by varying the sampling rate at runtime, while the proposed control strategies can guarantee co-regulated system stability when working under a time-varying sampling rate.
机译:“共调节”是一个时变周期采样策略,其中响应于受控系统的性能而动态地调整采样率。用于共调节系统的控制器需要调整与电流(更改)采样率对应的控制输出。这使得性能保证如难以获得的稳定性。在本文中,我们开发了两个用于共调节系统的稳定性保证控制算法。首先是一个正确的施工控制器,其中控制增益矩阵是一组采样率的离线预先计算。该方法允许任意切换采样率,但由于结果可以过于保守。然后,针对在后退地平线内预测系统状态轨迹和采样率(调度参数)轨迹的共调节系统定制了混合模型预测控制(MPC)算法。对所提出的控制器的性能进行了说明和讨论了共调节的多变化无人机系统(UAS)。结果表明共调节可以通过在运行时改变采样率来有效地重新分配计算资源,而建议的控制策略可以保证在时变抽样率下工作时的共调节系统稳定性。

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